API reference

Categories

TikTok Shop categories ranked by revenue and growth, the category tree with ids, and a category's top products.

5 routes
1 credit per call
Base URL https://api.tokconnect.com/v1

TikTok Shop category trends

Shop Analytics

The top TikTok Shop categories by estimated revenue over the last 7 days of data: revenue, units, growth, revenue per shop, top-3 and top-10 shop share, split by video, LIVE and affiliate. method=tree lists category names and ids (pass them as category to product_top_selling or creator_top_selling); method=products gives a category's top product ids; method=market_size counts what the source tracks in a market, not all of TikTok Shop. region picks the market (default US). Example: {"method":"tree","keyword":"skincare"}.

GET/v1/categories/market-size

How many products, shops, creators, videos, LIVEs and brands the source tracks

1 credit per call
Shop Analytics
17 markets

Parameters

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
endstring
Shop Analytics methods: custom range end, YYYY-MM-DD (capped at the last complete day).
regionstring
Market as an ISO country code, default US. Shop Analytics methods: sales data for that market, amounts in its currency. Live methods that take it (search, video detail, creator posts, Shop pages): TikTok as seen in that market where TikTok serves it. Core: US, GB, DE, FR, ES, JP, TH, VN, ID, MY, PH, BR, MX. Extended: PL, IT, NL, IE. Other countries are refused. See shop_regions.
startstring
Shop Analytics methods: custom range start, YYYY-MM-DD.
windowstring
Shop Analytics methods: date range ending at the last complete day (about 2 days ago). Default 30d. Use start/end for a custom range.
One of7d30d90d180d365d

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the category_trends tool with method: market_size. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "category_trends",
  "arguments": {
    "method": "market_size"
  }
}

Request

curl "https://api.tokconnect.com/v1/categories/market-size" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/categories/market-size",
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const res = await fetch("https://api.tokconnect.com/v1/categories/market-size", {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "counts": {
      "brands": 21214,
      "creators": 277023,
      "livestreams": 191201,
      "products": 1041879,
      "shops": 114343,
      "videos": 1441803
    },
    "dataEnd": "2026-10-28",
    "market": "US",
    "notes": [
      "Market US (United States)."
    ],
    "scope": "Totals tracked by the data source in this market, not all of TikTok Shop. They size the source's own coverage (items it follows), not sales, and not every seller, creator or video on TikTok.",
    "source": "TokConnect Shop Analytics (TikTok Shop US). Revenue and units are estimates: units track TikTok's sold counter; revenue is units times listed price."
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.countsobject
data.counts.brandsinteger
data.counts.creatorsinteger
data.counts.livestreamsinteger
data.counts.productsinteger
data.counts.shopsinteger
data.counts.videosinteger
data.dataEndstring
data.marketstring
data.notesarray
data.scopestring
data.sourcestring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/categories/products

A category's top product ids (US market only)

1 credit per call
Shop Analytics
17 markets

Parameters

categorystring
method=products: category name exactly as method=list returns it (cate_id), e.g. 'Beauty & Personal Care'.

5 more optional parameters

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
endstring
Shop Analytics methods: custom range end, YYYY-MM-DD (capped at the last complete day).
regionstring
Market as an ISO country code, default US. Shop Analytics methods: sales data for that market, amounts in its currency. Live methods that take it (search, video detail, creator posts, Shop pages): TikTok as seen in that market where TikTok serves it. Core: US, GB, DE, FR, ES, JP, TH, VN, ID, MY, PH, BR, MX. Extended: PL, IT, NL, IE. Other countries are refused. See shop_regions.
startstring
Shop Analytics methods: custom range start, YYYY-MM-DD.
windowstring
Shop Analytics methods: date range ending at the last complete day (about 2 days ago). Default 30d. Use start/end for a custom range.
One of7d30d90d180d365d

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the category_trends tool with method: products. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "category_trends",
  "arguments": {
    "method": "products",
    "category": "Beauty & Personal Care"
  }
}

Request

curl "https://api.tokconnect.com/v1/categories/products?category=Beauty%20%26%20Personal%20Care" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/categories/products",
    params={
        "category": "Beauty & Personal Care",
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  category: "Beauty & Personal Care",
});
const res = await fetch(`https://api.tokconnect.com/v1/categories/products?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "category": "Beauty & Personal Care",
    "currency": "USD",
    "dataEnd": "2026-10-28",
    "groups": [
      {
        "cate_id": "Beauty & Personal Care",
        "product": [
          {
            "id": "1732738686074327162"
          },
          {
            "id": "1732738538912714757"
          },
          {
            "id": "1732052189676081387"
          }
        ]
      }
    ],
    "market": "US",
    "notes": [
      "Market US (United States): amounts are in USD."
    ],
    "see_also": "Pass these ids to product method=sales with ids=[...] for revenue and units, or product_top_selling with a keyword for a full ranking.",
    "source": "TokConnect Shop Analytics (TikTok Shop US). Revenue and units are estimates: units track TikTok's sold counter; revenue is units times listed price.",
    "window": {
      "days": 30,
      "end": "2026-10-28",
      "start": "2026-09-29"
    }
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.categorystring
data.currencystring
data.dataEndstring
data.groupsarray
data.groups[].cate_idstring
data.groups[].productarray
data.groups[].product[].idstring
data.marketstring
data.notesarray
data.see_alsostring
data.sourcestring
data.windowobject
data.window.daysinteger
data.window.endstring
data.window.startstring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/categories/search

Find category names and ids by keyword

1 credit per call
Shop Analytics
17 markets

Parameters

keywordstring
method=search: category name to look up in the data source, e.g. 'serum'. method=tree: text to find in category names, e.g. 'skincare'.

2 more optional parameters

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
regionstring
Market as an ISO country code, default US. Shop Analytics methods: sales data for that market, amounts in its currency. Live methods that take it (search, video detail, creator posts, Shop pages): TikTok as seen in that market where TikTok serves it. Core: US, GB, DE, FR, ES, JP, TH, VN, ID, MY, PH, BR, MX. Extended: PL, IT, NL, IE. Other countries are refused. See shop_regions.

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the category_trends tool with method: search. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "category_trends",
  "arguments": {
    "method": "search",
    "keyword": "lip oil"
  }
}

Request

curl "https://api.tokconnect.com/v1/categories/search?keyword=lip%20oil" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/categories/search",
    params={
        "keyword": "lip oil",
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  keyword: "lip oil",
});
const res = await fetch(`https://api.tokconnect.com/v1/categories/search?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "currency": "USD",
    "items": [
      {
        "id": 805640,
        "name": "Kids' Fashion-Boys' Footwear-Slippers",
        "score": 0
      },
      {
        "id": 806536,
        "name": "Kids' Fashion-Girls' Footwear-Slippers",
        "score": 0
      },
      {
        "id": 601374,
        "name": "Shoes-Men's Shoes-Slippers",
        "score": 0
      }
    ],
    "keyword": "lip oil",
    "market": "US",
    "notes": [
      "Market US (United States): amounts are in USD."
    ],
    "source": "TokConnect Shop Analytics (TikTok Shop US). Revenue and units are estimates: units track TikTok's sold counter; revenue is units times listed price."
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.currencystring
data.itemsarray
data.items[].idinteger
data.items[].namestring
data.items[].scoreinteger
data.keywordstring
data.marketstring
data.notesarray
data.sourcestring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/categories/top

Categories with revenue, growth and concentration

1 credit per call
Shop Analytics
17 markets

Parameters

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
endstring
Shop Analytics methods: custom range end, YYYY-MM-DD (capped at the last complete day).
levelstring
method=list or tree: keep only categories of this level (1 = top level, 3 = most specific).
One of123
regionstring
Market as an ISO country code, default US. Shop Analytics methods: sales data for that market, amounts in its currency. Live methods that take it (search, video detail, creator posts, Shop pages): TikTok as seen in that market where TikTok serves it. Core: US, GB, DE, FR, ES, JP, TH, VN, ID, MY, PH, BR, MX. Extended: PL, IT, NL, IE. Other countries are refused. See shop_regions.
startstring
Shop Analytics methods: custom range start, YYYY-MM-DD.
windowstring
Shop Analytics methods: date range ending at the last complete day (about 2 days ago). Default 30d. Use start/end for a custom range.
One of7d30d90d180d365d

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the category_trends tool with method: list. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "category_trends",
  "arguments": {
    "method": "list"
  }
}

Request

curl "https://api.tokconnect.com/v1/categories/top" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/categories/top",
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const res = await fetch("https://api.tokconnect.com/v1/categories/top", {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "count": 10,
    "currency": "USD",
    "dataEnd": "2026-10-28",
    "items": [
      {
        "affiliate_revenue": "$72.32m",
        "average_revenue": "$5.67k",
        "cate_id": "Beauty & Personal Care",
        "date_interval": "7d",
        "id": "601450",
        "level": "1",
        "live_revenue": "$16.40m",
        "name": "Beauty & Personal Care",
        "revenue": "$89.73m",
        "revenue_growth_rate": -0.1423943532521982,
        "revenue_trend": [
          1222082970,
          1304434512,
          1093114419
        ],
        "self_promotion_revenue": "$7.23m",
        "shop_number": 15829,
        "shopping_mall_revenue": "$10.19m",
        "showcase_revenue": "$12.93m",
        "top_10_shop_revenue_ratio": 0.184013778054234,
        "top_3_shop_revenue_ratio": 0.09835574995436176,
        "video_revenue": "$60.40m"
      },
      {
        "affiliate_revenue": "$59.36m",
        "average_revenue": "$5.39k",
        "cate_id": "Womenswear & Underwear",
        "date_interval": "7d",
        "id": "601152",
        "level": "1",
        "live_revenue": "$9.50m",
        "name": "Womenswear & Underwear",
        "revenue": "$73.80m",
        "revenue_growth_rate": -0.21692984191843792,
        "revenue_trend": [
          946945250,
          1094214841.5,
          827028189
        ],
        "self_promotion_revenue": "$7.17m",
        "shop_number": 13701,
        "shopping_mall_revenue": "$7.26m",
        "showcase_revenue": "$10.49m",
        "top_10_shop_revenue_ratio": 0.15677009265967165,
        "top_3_shop_revenue_ratio": 0.07922574659084249,
        "video_revenue": "$53.81m"
      },
      {
        "affiliate_revenue": "$25.90m",
        "average_revenue": "$2.66k",
        "cate_id": "Sports & Outdoor",
        "date_interval": "7d",
        "id": "603014",
        "level": "1",
        "live_revenue": "$4.88m",
        "name": "Sports & Outdoor",
        "revenue": "$34.18m",
        "revenue_growth_rate": -0.10448608774096589,
        "revenue_trend": [
          483660312,
          505927939,
          419452406
        ],
        "self_promotion_revenue": "$3.31m",
        "shop_number": 12859,
        "shopping_mall_revenue": "$4.97m",
        "showcase_revenue": "$6.87m",
        "top_10_shop_revenue_ratio": 0.20453100930944212,
        "top_3_shop_revenue_ratio": 0.11515856825031943,
        "video_revenue": "$22.43m"
      }
    ],
    "market": "US",
    "notes": [
      "Market US (United States): amounts are in USD.",
      "Category figures always cover the last 7 days of data (window below), whatever window you ask for. For a longer view of one category, rank its products with product_top_selling and a keyword."
    ],
    "see_also": "Pass a row's cate_id as category to category_trends method=products for its top product ids.",
    "source": "TokConnect Shop Analytics (TikTok Shop US). Revenue and units are estimates: units track TikTok's sold counter; revenue is units times listed price.",
    "window": {
      "days": 7,
      "end": "2026-10-28",
      "start": "2026-10-22"
    }
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.countinteger
data.currencystring
data.dataEndstring
data.itemsarray
data.items[].affiliate_revenuestring
data.items[].average_revenuestring
data.items[].cate_idstring
data.items[].date_intervalstring
data.items[].idstring
data.items[].levelstring
data.items[].live_revenuestring
data.items[].namestring
data.items[].revenuestring
data.items[].revenue_growth_ratenumber
data.items[].revenue_trendarray
data.items[].self_promotion_revenuestring
data.items[].shop_numberinteger
data.items[].shopping_mall_revenuestring
data.items[].showcase_revenuestring
data.items[].top_10_shop_revenue_rationumber
data.items[].top_3_shop_revenue_rationumber
data.items[].video_revenuestring
data.marketstring
data.notesarray
data.see_alsostring
data.sourcestring
data.windowobject
data.window.daysinteger
data.window.endstring
data.window.startstring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

400
401
402
403
404
429
502
503

Try it Live call, 1 credit

GET/v1/categories/tree

Category names and ids to use as category

1 credit per call
Shop Analytics

Parameters

limitinteger
method=tree: max categories to return (default 50).

3 more optional parameters

detailstring
compact (default) returns the useful fields; full adds the raw upstream payload (large).
Defaultcompact One ofcompactfull
keywordstring
method=search: category name to look up in the data source, e.g. 'serum'. method=tree: text to find in category names, e.g. 'skincare'.
levelstring
method=list or tree: keep only categories of this level (1 = top level, 3 = most specific).
One of123

Use it in Claude or ChatGPT

Connect https://mcp.tokconnect.com/mcp and your AI calls this as the category_trends tool with method: tree. Same data, same credit. MCP setup

MCP tool call

{
  "tool": "category_trends",
  "arguments": {
    "method": "tree",
    "limit": "3"
  }
}

Request

curl "https://api.tokconnect.com/v1/categories/tree?limit=3" \
  -H "X-API-Key: $TOKCONNECT_API_KEY"

import os
import requests

r = requests.get(
    "https://api.tokconnect.com/v1/categories/tree",
    params={
        "limit": "3",
    },
    headers={"X-API-Key": os.environ["TOKCONNECT_API_KEY"]},
    timeout=100,
)
r.raise_for_status()
body = r.json()
print(body["data"])

const params = new URLSearchParams({
  limit: "3",
});
const res = await fetch(`https://api.tokconnect.com/v1/categories/tree?${params}`, {
  headers: { "X-API-Key": process.env.TOKCONNECT_API_KEY },
});
if (!res.ok) throw new Error((await res.json()).error?.message);
const { data, next_cursor } = await res.json();

Response

Example response

{
  "data": {
    "count": 3,
    "hasMore": true,
    "items": [
      {
        "id": "600001",
        "level": 1,
        "name": "Home Supplies"
      },
      {
        "id": "600024",
        "level": 1,
        "name": "Kitchenware"
      },
      {
        "id": "600154",
        "level": 1,
        "name": "Textiles & Soft Furnishings"
      }
    ],
    "matched": 26,
    "see_also": "Pass an id or name as category to product_top_selling or creator_top_selling. Names are the US English category names; ids are the source's own.",
    "source": "TokConnect Shop Analytics (TikTok Shop US). Revenue and units are estimates: units track TikTok's sold counter; revenue is units times listed price."
  },
  "meta": {
    "credits_charged": 1,
    "credits_remaining": 7906,
    "request_id": "req_3f9c2a1b7d4e"
  }
}

Response fields

dataobjectThe tool's result. Compact by default; pass detail=full for the complete record.
data.countinteger
data.hasMoreboolean
data.itemsarray
data.items[].idstring
data.items[].levelinteger
data.items[].namestring
data.matchedinteger
data.see_alsostring
data.sourcestring
metaobject
meta.credits_chargedinteger1 on success.
meta.credits_remainingintegerCredits left this period, when known.
meta.request_idstringQuote it to support.
next_cursorstringPresent when there is another page: pass it back as cursor.

Errors

Try it Live call, 1 credit